Abnormalities in Automatic Processing of Illness-Related Stimuli in Self-Rated Alexithymia
Bibliographic record
Abstract
AIM: To investigate abnormalities in automatic information processing related to self- and observer-rated alexithymia, especially with regard to somatization, controlling for confounding variables such as depression and affect. SAMPLE: 89 healthy subjects (60% female), aged 19-71 years (M = 32.1). 58 subjects were additionally rated by an observer. MEASURES: Alexithymia (self-rating: TAS-20, observer rating: OAS); automatic information processing (priming task including verbal [illness-related, negative, positive, neutral] and facial [negative, positive, neutral] stimuli); somatoform symptoms (SOMS-7T); confounders: depression (BDI), affect (PANAS). RESULTS: Higher self-reported alexithymia scores were associated with lower reaction times for negative (r = .19, p < .10) and positive (r = .26, p < .05) verbal primes when the target was illness-related. Self-reported alexithymia was correlated with number (r = .42, p < .01) and intensity of current somatoform symptoms (r = .36, p < .01), but unrelated to observer-rated alexithymia (r = .11, p = .42). DISCUSSION: Results indicate a faster allocation of attentional resources away from task-irrelevant information towards illness-related stimuli in alexithymia. Considering the close relationship between alexithymia and somatization, these findings are compatible with the theoretical view that alexithymics focus strongly on bodily sensations of emotional arousal. A single observer rating (OAS) does not seem to be an adequate alexithymia-measure in community samples.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".